{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T06:50:46Z","timestamp":1766731846947,"version":"3.48.0"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T00:00:00Z","timestamp":1766707200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T00:00:00Z","timestamp":1766707200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Ministry of Science and Technology, Taiwan","award":["MOST110-2314-B-016-010-MY3"],"award-info":[{"award-number":["MOST110-2314-B-016-010-MY3"]}]},{"name":"Ministry of Science and Technology, Taiwan","award":["MOST110-2321-B-016-002"],"award-info":[{"award-number":["MOST110-2321-B-016-002"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Med Syst"],"DOI":"10.1007\/s10916-025-02333-6","type":"journal-article","created":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T06:47:23Z","timestamp":1766731643000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence-Enabled Electrocardiography Identifies Osteoporosis and has Prognostic Value"],"prefix":"10.1007","volume":"49","author":[{"given":"Shi-Chue","family":"Hsing","sequence":"first","affiliation":[]},{"given":"Dung-Jang","family":"Tsai","sequence":"additional","affiliation":[]},{"given":"Chin","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Chin-Sheng","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Chia-Cheng","family":"Lee","sequence":"additional","affiliation":[]},{"given":"Chih-Hung","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Wen-Hui","family":"Fang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,12,26]]},"reference":[{"issue":"5 Suppl","key":"2333_CR1","first-page":"505s-511s","volume":"17","author":"BL Riggs","year":"1995","unstructured":"Riggs BL, Melton LJ, 3rd: The worldwide problem of osteoporosis: insights afforded by epidemiology. Bone 1995, 17(5 Suppl):505s-511s.","journal-title":"Bone"},{"issue":"9","key":"2333_CR2","doi-asserted-by":"publisher","first-page":"955","DOI":"10.1111\/j.1532-5415.1995.tb05557.x","volume":"43","author":"GA Greendale","year":"1995","unstructured":"Greendale GA, Barrett-Connor E, Ingles S, Haile R: Late physical and functional effects of osteoporotic fracture in women: the Rancho Bernardo Study. Journal of the American Geriatrics Society 1995, 43(9):955\u2013961.","journal-title":"Journal of the American Geriatrics Society"},{"key":"2333_CR3","doi-asserted-by":"crossref","unstructured":"Osteoporosis: review of the evidence for prevention, diagnosis and treatment and cost-effectiveness analysis. Executive summary. Osteoporosis international: a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA 1998, 8 Suppl 4:S3-6.","DOI":"10.1007\/PL00022721"},{"issue":"22","key":"2333_CR4","doi-asserted-by":"publisher","first-page":"2815","DOI":"10.1001\/jama.286.22.2815","volume":"286","author":"ES Siris","year":"2001","unstructured":"Siris ES, Miller PD, Barrett-Connor E, Faulkner KG, Wehren LE, Abbott TA, Berger ML, Santora AC, Sherwood LM: Identification and fracture outcomes of undiagnosed low bone mineral density in postmenopausal women: results from the National Osteoporosis Risk Assessment. Jama 2001, 286(22):2815\u20132822.","journal-title":"Jama"},{"issue":"9321","key":"2333_CR5","doi-asserted-by":"publisher","first-page":"1929","DOI":"10.1016\/S0140-6736(02)08761-5","volume":"359","author":"JA Kanis","year":"2002","unstructured":"Kanis JA: Diagnosis of osteoporosis and assessment of fracture risk. The Lancet 2002, 359(9321):1929\u20131936.","journal-title":"The Lancet"},{"issue":"8","key":"2333_CR6","doi-asserted-by":"publisher","first-page":"1106","DOI":"10.1111\/j.1524-4733.2009.00577.x","volume":"12","author":"D Mueller","year":"2009","unstructured":"Mueller D, Gandjour A: Cost-effectiveness of using clinical risk factors with and without DXA for osteoporosis screening in postmenopausal women. Value in Health 2009, 12(8):1106\u20131117.","journal-title":"Value in Health"},{"key":"2333_CR7","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s11657-012-0109-9","volume":"7","author":"H Orimo","year":"2012","unstructured":"Orimo H, Nakamura T, Hosoi T, Iki M, Uenishi K, Endo N, Ohta H, Shiraki M, Sugimoto T, Suzuki T: Japanese 2011 guidelines for prevention and treatment of osteoporosis\u2014executive summary. Archives of osteoporosis 2012, 7:3\u201320.","journal-title":"Archives of osteoporosis"},{"issue":"5","key":"2333_CR8","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1046\/j.1525-1446.2000.00398.x","volume":"17","author":"CA Sedlak","year":"2000","unstructured":"Sedlak CA, Doheny MO, Jones SL: Osteoporosis education programs: changing knowledge and behaviors. Public health nursing 2000, 17(5):398\u2013402.","journal-title":"Public health nursing"},{"issue":"5","key":"2333_CR9","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1001\/jamacardio.2019.0640","volume":"4","author":"CD Galloway","year":"2019","unstructured":"Galloway CD, Valys AV, Shreibati JB, Treiman DL, Petterson FL, Gundotra VP, Albert DE, Attia ZI, Carter RE, Asirvatham SJ: Development and validation of a deep-learning model to screen for hyperkalemia from the electrocardiogram. JAMA cardiology 2019, 4(5):428\u2013436.","journal-title":"JAMA cardiology"},{"issue":"1","key":"2333_CR10","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1038\/s41591-018-0268-3","volume":"25","author":"AY Hannun","year":"2019","unstructured":"Hannun AY, Rajpurkar P, Haghpanahi M, Tison GH, Bourn C, Turakhia MP, Ng AY: Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature medicine 2019, 25(1):65\u201369.","journal-title":"Nature medicine"},{"issue":"3","key":"2333_CR11","doi-asserted-by":"publisher","first-page":"e15931","DOI":"10.2196\/15931","volume":"8","author":"C-S Lin","year":"2020","unstructured":"Lin C-S, Lin C, Fang W-H, Hsu C-J, Chen S-J, Huang K-H, Lin W-S, Tsai C-S, Kuo C-C, Chau T: A deep-learning algorithm (ECG12Net) for detecting hypokalemia and hyperkalemia by electrocardiography: algorithm development. JMIR medical informatics 2020, 8(3):e15931.","journal-title":"JMIR medical informatics"},{"issue":"9","key":"2333_CR12","doi-asserted-by":"publisher","first-page":"765","DOI":"10.4244\/EIJ-D-20-01155","volume":"17","author":"W-C Liu","year":"2021","unstructured":"Liu W-C, Lin C-S, Tsai C-S, Tsao T-P, Cheng C-C, Liou J-T, Lin W-S, Cheng S-M, Lou Y-S, Lee C-C: A deep learning algorithm for detecting acute myocardial infarction. EuroIntervention 2021, 17(9):765\u2013773.","journal-title":"EuroIntervention"},{"issue":"9","key":"2333_CR13","first-page":"e007284","volume":"12","author":"ZI Attia","year":"2019","unstructured":"Attia ZI, Friedman PA, Noseworthy PA, Lopez-Jimenez F, Ladewig DJ, Satam G, Pellikka PA, Munger TM, Asirvatham SJ, Scott CG: Age and sex estimation using artificial intelligence from standard 12-lead ECGs. Circulation: Arrhythmia and Electrophysiology 2019, 12(9):e007284.","journal-title":"Circulation: Arrhythmia and Electrophysiology"},{"issue":"1","key":"2333_CR14","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1038\/s41591-018-0240-2","volume":"25","author":"ZI Attia","year":"2019","unstructured":"Attia ZI, Kapa S, Lopez-Jimenez F, McKie PM, Ladewig DJ, Satam G, Pellikka PA, Enriquez-Sarano M, Noseworthy PA, Munger TM: Screening for cardiac contractile dysfunction using an artificial intelligence\u2013enabled electrocardiogram. Nature medicine 2019, 25(1):70\u201374.","journal-title":"Nature medicine"},{"issue":"10201","key":"2333_CR15","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/S0140-6736(19)31721-0","volume":"394","author":"ZI Attia","year":"2019","unstructured":"Attia ZI, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Deshmukh AJ, Gersh BJ, Carter RE, Yao X, Rabinstein AA, Erickson BJ: An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. The Lancet 2019, 394(10201):861\u2013867.","journal-title":"The Lancet"},{"issue":"7","key":"2333_CR16","doi-asserted-by":"publisher","first-page":"e014717","DOI":"10.1161\/JAHA.119.014717","volume":"9","author":"JM Kwon","year":"2020","unstructured":"Kwon JM, Lee SY, Jeon KH, Lee Y, Kim KH, Park J, Oh BH, Lee MM: Deep learning\u2013based algorithm for detecting aortic stenosis using electrocardiography. Journal of the American Heart Association 2020, 9(7):e014717.","journal-title":"Journal of the American Heart Association"},{"issue":"3","key":"2333_CR17","doi-asserted-by":"publisher","first-page":"455","DOI":"10.3390\/jpm12030455","volume":"12","author":"H-Y Chen","year":"2022","unstructured":"Chen H-Y, Lin C-S, Fang W-H, Lou Y-S, Cheng C-C, Lee C-C, Lin C: Artificial intelligence-enabled electrocardiography predicts left ventricular dysfunction and future cardiovascular outcomes: a retrospective analysis. Journal of Personalized Medicine 2022, 12(3):455.","journal-title":"Journal of Personalized Medicine"},{"issue":"2","key":"2333_CR18","doi-asserted-by":"publisher","first-page":"394","DOI":"10.3390\/biomedicines10020394","volume":"10","author":"P-S Huang","year":"2022","unstructured":"Huang P-S, Tseng Y-H, Tsai C-F, Chen J-J, Yang S-C, Chiu F-C, Chen Z-W, Hwang J-J, Chuang EY, Wang Y-C: An artificial intelligence-enabled ECG algorithm for the prediction and localization of angiography-proven coronary artery disease. Biomedicines 2022, 10(2):394.","journal-title":"Biomedicines"},{"issue":"4","key":"2333_CR19","doi-asserted-by":"publisher","first-page":"3317","DOI":"10.1007\/s00068-022-01904-3","volume":"48","author":"C-C Lee","year":"2022","unstructured":"Lee C-C, Lin C-S, Tsai C-S, Tsao T-P, Cheng C-C, Liou J-T, Lin W-S, Lee C-C, Chen J-T, Lin C: A deep learning-based system capable of detecting pneumothorax via electrocardiogram. European Journal of Trauma and Emergency Surgery 2022, 48(4):3317\u20133326.","journal-title":"European Journal of Trauma and Emergency Surgery"},{"issue":"2","key":"2333_CR20","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.cjca.2021.09.028","volume":"38","author":"W-T Liu","year":"2022","unstructured":"Liu W-T, Lin C-S, Tsao T-P, Lee C-C, Cheng C-C, Chen J-T, Tsai C-S, Lin W-S, Lin C: A deep-learning algorithm-enhanced system integrating electrocardiograms and chest X-rays for diagnosing aortic dissection. Canadian Journal of Cardiology 2022, 38(2):160\u2013168.","journal-title":"Canadian Journal of Cardiology"},{"issue":"5","key":"2333_CR21","first-page":"673","volume":"3","author":"DE Warburton","year":"2007","unstructured":"Warburton DE, Nicol CW, Gatto SN, Bredin SS: Cardiovascular disease and osteoporosis: balancing risk management. Vascular health and risk management 2007, 3(5):673\u2013689.","journal-title":"Vascular health and risk management"},{"key":"2333_CR22","unstructured":"Organization WH: WHO scientific group on the assessment of osteoporosis at primary health care level. In: Summary meeting report: 2004; 2004: 5\u20137."},{"key":"2333_CR23","doi-asserted-by":"publisher","first-page":"754909","DOI":"10.3389\/fcvm.2022.754909","volume":"9","author":"CH Chang","year":"2022","unstructured":"Chang CH, Lin CS, Luo YS, Lee YT, Lin C: Electrocardiogram-based heart age estimation by a deep learning model provides more information on the incidence of cardiovascular disorders. Front Cardiovasc Med 2022, 9:754909.","journal-title":"Front Cardiovasc Med"},{"issue":"7623","key":"2333_CR24","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1038\/538020a","volume":"538","author":"D Castelvecchi","year":"2016","unstructured":"Castelvecchi D: Can we open the black box of AI? Nature News 2016, 538(7623):20.","journal-title":"Nature News"},{"issue":"1","key":"2333_CR25","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1002\/jbmr.4164","volume":"36","author":"Z Raisi-Estabragh","year":"2021","unstructured":"Raisi-Estabragh Z, Biasiolli L, Cooper J, Aung N, Fung K, Paiva JM, Sanghvi MM, Thomson RJ, Curtis E, Paccou J: Poor bone quality is associated with greater arterial stiffness: insights from the UK Biobank. Journal of Bone and Mineral Research 2021, 36(1):90\u201399.","journal-title":"Journal of Bone and Mineral Research"},{"key":"2333_CR26","doi-asserted-by":"crossref","unstructured":"Doherty TM, Asotra K, Fitzpatrick LA, Qiao J-H, Wilkin DJ, Detrano RC, Dunstan CR, Shah PK, Rajavashisth TB: Calcification in atherosclerosis: bone biology and chronic inflammation at the arterial crossroads. Proceedings of the National Academy of Sciences 2003, 100(20):11201\u201311206.","DOI":"10.1073\/pnas.1932554100"},{"issue":"7","key":"2333_CR27","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1093\/qjmed\/hci077","volume":"98","author":"D Hamerman","year":"2005","unstructured":"Hamerman D: Osteoporosis and atherosclerosis: biological linkages and the emergence of dual-purpose therapies. Qjm 2005, 98(7):467\u2013484.","journal-title":"Qjm"},{"key":"2333_CR28","doi-asserted-by":"publisher","first-page":"779","DOI":"10.2165\/00007256-200434120-00001","volume":"34","author":"C Whitney","year":"2004","unstructured":"Whitney C, Warburton DE, Frohlich J, Chan SY, McKay H, Khan K: Are cardiovascular disease and osteoporosis directly linked? Sports medicine 2004, 34:779\u2013807.","journal-title":"Sports medicine"},{"issue":"11","key":"2333_CR29","doi-asserted-by":"publisher","first-page":"2346","DOI":"10.1161\/01.ATV.20.11.2346","volume":"20","author":"F Parhami","year":"2000","unstructured":"Parhami F, Garfinkel A, Demer LL: Role of lipids in osteoporosis. Arteriosclerosis, thrombosis, and vascular biology 2000, 20(11):2346\u20132348.","journal-title":"Arteriosclerosis, thrombosis, and vascular biology"},{"issue":"2","key":"2333_CR30","doi-asserted-by":"publisher","first-page":"e6-e10","DOI":"10.1161\/01.ATV.0000112023.62695.7f","volume":"24","author":"Y Tintut","year":"2004","unstructured":"Tintut Y, Morony S, Demer LL: Hyperlipidemia promotes osteoclastic potential of bone marrow cells ex vivo. Arteriosclerosis, thrombosis, and vascular biology 2004, 24(2):e6-e10.","journal-title":"Arteriosclerosis, thrombosis, and vascular biology"},{"issue":"7","key":"2333_CR31","doi-asserted-by":"publisher","first-page":"570","DOI":"10.1161\/01.RES.0000036607.05037.DA","volume":"91","author":"F Parhami","year":"2002","unstructured":"Parhami F, Basseri B, Hwang J, Tintut Y, Demer LL: High-density lipoprotein regulates calcification of vascular cells. Circulation research 2002, 91(7):570\u2013576.","journal-title":"Circulation research"},{"key":"2333_CR32","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1007\/s00198-006-0255-2","volume":"18","author":"Y Bagger","year":"2007","unstructured":"Bagger Y, Rasmussen HB, Alexandersen P, Werge T, Christiansen C, Tanko L, Group PS: Links between cardiovascular disease and osteoporosis in postmenopausal women: serum lipids or atherosclerosis per se? Osteoporosis international 2007, 18:505\u2013512.","journal-title":"Osteoporosis international"},{"key":"2333_CR33","doi-asserted-by":"crossref","unstructured":"Rubin MR, Silverberg SJ: Vascular calcification and osteoporosis\u2014the nature of the nexus. In., vol. 89: Oxford University Press; 2004: 4243\u20134245.","DOI":"10.1210\/jc.2004-1324"},{"key":"2333_CR34","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.ijcard.2020.12.065","volume":"329","author":"IZ Attia","year":"2021","unstructured":"Attia IZ, Tseng AS, Benavente ED, Medina-Inojosa JR, Clark TG, Malyutina S, Kapa S, Schirmer H, Kudryavtsev AV, Noseworthy PA: External validation of a deep learning electrocardiogram algorithm to detect ventricular dysfunction. International journal of cardiology 2021, 329:130\u2013135.","journal-title":"International journal of cardiology"},{"key":"2333_CR35","doi-asserted-by":"crossref","unstructured":"Larrazabal AJ, Nieto N, Peterson V, Milone DH, Ferrante E: Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis. Proceedings of the National Academy of Sciences 2020, 117(23):12592\u201312594.","DOI":"10.1073\/pnas.1919012117"},{"key":"2333_CR36","doi-asserted-by":"crossref","unstructured":"Lee Y-T, Lin C-S, Fang W-H, Lee C-C, Ho C-L, Wang C-H, Tsai DJ, Lin C: Artificial intelligence-enabled electrocardiography detects hypoalbuminemia and identifies the mechanism of hepatorenal and cardiovascular events. Frontiers in Cardiovascular Medicine 2022:1530.","DOI":"10.3389\/fcvm.2022.895201"},{"issue":"8763","key":"2333_CR37","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1016\/0140-6736(91)90489-C","volume":"338","author":"W Browner","year":"1991","unstructured":"Browner W, Seeley D, Cummings S, Vogt T, Group SoOFR: Non-trauma mortality in elderly women with low bone mineral density. The Lancet 1991, 338(8763):355\u2013358.","journal-title":"The Lancet"},{"issue":"10","key":"2333_CR38","doi-asserted-by":"publisher","first-page":"1974","DOI":"10.1359\/jbmr.2000.15.10.1974","volume":"15","author":"DM Kado","year":"2000","unstructured":"Kado DM, Browner WS, Blackwell T, Gore R, Cummings SR: Rate of bone loss is associated with mortality in older women: a prospective study. Journal of Bone and Mineral Research 2000, 15(10):1974\u20131980.","journal-title":"Journal of Bone and Mineral Research"},{"issue":"3","key":"2333_CR39","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/S0002-9343(99)00028-5","volume":"106","author":"P von der Recke","year":"1999","unstructured":"von der Recke P, Hansen MA, Hassager C: The association between low bone mass at the menopause and cardiovascular mortality. The American journal of medicine 1999, 106(3):273\u2013278.","journal-title":"The American journal of medicine"},{"issue":"3","key":"2333_CR40","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1007\/s002239900513","volume":"63","author":"C Johansson","year":"1998","unstructured":"Johansson C, Black D, Johnell O, Od\u00e9n A, Mellstr\u00f6m D: Bone mineral density is a predictor of survival. Calcif Tissue Int 1998, 63(3):190\u2013196.","journal-title":"Calcif Tissue Int"},{"key":"2333_CR41","doi-asserted-by":"crossref","unstructured":"Schulze-Hagen MF, Roderburg C, Wirtz TH, J\u00f6rdens MS, B\u00fcndgens L, Abu Jhaisha S, Hohlstein P, Brozat JF, Bruners P, Loberg C et al: Decreased Bone Mineral Density Is a Predictor of Poor Survival in Critically Ill Patients. J Clin Med 2021, 10(16).","DOI":"10.3390\/jcm10163741"},{"issue":"1","key":"2333_CR42","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1038\/s41746-021-00550-0","volume":"5","author":"C Lin","year":"2022","unstructured":"Lin C, Chau T, Lin C-S, Shang H-S, Fang W-H, Lee D-J, Lee C-C, Tsai S-H, Wang C-H, Lin S-H: Point-of-care artificial intelligence-enabled ECG for dyskalemia: A retrospective cohort analysis for accuracy and outcome prediction. npj Digital Medicine 2022, 5(1):8.","journal-title":"npj Digital Medicine"},{"issue":"1","key":"2333_CR43","doi-asserted-by":"publisher","first-page":"5117","DOI":"10.1038\/s41467-021-25351-7","volume":"12","author":"EM Lima","year":"2021","unstructured":"Lima EM, Ribeiro AH, Paix\u00e3o GM, Ribeiro MH, Pinto-Filho MM, Gomes PR, Oliveira DM, Sabino EC, Duncan BB, Giatti L: Deep neural network-estimated electrocardiographic age as a mortality predictor. Nature communications 2021, 12(1):5117.","journal-title":"Nature communications"},{"issue":"1","key":"2333_CR44","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1046\/j.1365-2281.1998.00067.x","volume":"18","author":"J\u00f8rgensen Prins","year":"1998","unstructured":"Prins, J\u00f8rgensen, J\u00f8rgensen, Hassager: The role of quantitative ultrasound in the assessment of bone: a review. Clinical Physiology 1998, 18(1):3\u201317.","journal-title":"Clinical Physiology"},{"issue":"2","key":"2333_CR45","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1385\/JCD:4:2:159","volume":"4","author":"II K Kim","year":"2001","unstructured":"Kim II K, Han I-K, Kim H, Cho NH: How reliable is the ultrasound densitometer for community screening to diagnose osteoporosis in spine, femur, and forearm? Journal of Clinical Densitometry 2001, 4(2):159\u2013165.","journal-title":"Journal of Clinical Densitometry"},{"issue":"3","key":"2333_CR46","first-page":"e007988","volume":"13","author":"PA Noseworthy","year":"2020","unstructured":"Noseworthy PA, Attia ZI, Brewer LC, Hayes SN, Yao X, Kapa S, Friedman PA, Lopez-Jimenez F: Assessing and mitigating bias in medical artificial intelligence: the effects of race and ethnicity on a deep learning model for ECG analysis. Circulation: Arrhythmia and Electrophysiology 2020, 13(3):e007988.","journal-title":"Circulation: Arrhythmia and Electrophysiology"}],"container-title":["Journal of Medical Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-025-02333-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10916-025-02333-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-025-02333-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T06:47:26Z","timestamp":1766731646000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10916-025-02333-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,26]]},"references-count":46,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["2333"],"URL":"https:\/\/doi.org\/10.1007\/s10916-025-02333-6","relation":{},"ISSN":["1573-689X"],"issn-type":[{"value":"1573-689X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,26]]},"assertion":[{"value":"4 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All methods were carried out in accordance with relevant guidelines and regulations. The need for informed consent was waived by the Ethics Committee\/Institutional Review Board of the institutional ethics committee of the Tri-Service General Hospital (C202105049).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Clinical trial number"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"191"}}